{
 "cells": [
  {
   "metadata": {},
   "cell_type": "markdown",
   "source": "## 数据准备",
   "id": "b0b9c0fa4d570b1b"
  },
  {
   "metadata": {
    "ExecuteTime": {
     "end_time": "2024-05-29T09:06:58.181976Z",
     "start_time": "2024-05-29T09:06:57.733810Z"
    }
   },
   "cell_type": "code",
   "source": [
    "from pandas import read_csv\n",
    "from datetime import datetime\n",
    "\n",
    "\n",
    "# 将字符串转换为datetime对象\n",
    "def parse(x):\n",
    "    return datetime.strptime(x, '%Y %m %d %H')\n",
    "\n",
    "\n",
    "# 使用read_csv函数读取'raw.csv'文件，并将其中包含'year', 'month', 'day', 'hour'的列作为日期列\n",
    "# index_col=0 表示将第一列设置为索引列\n",
    "# date_parser=parse 表示使用自定义的 parse 函数来解析日期列\n",
    "dataset = read_csv('data/空气质量预测/raw.csv', parse_dates=[['year', 'month', 'day', 'hour']], index_col=0, date_parser=parse)\n",
    "print(dataset.head(5))\n",
    "\n",
    "# 使用drop函数删除名为'No'的列，axis=1表示操作的是列，inplace=True表示原地修改\n",
    "dataset.drop('No', axis=1, inplace=True)\n",
    "print(dataset.head(5))"
   ],
   "id": "initial_id",
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\shey\\AppData\\Local\\Temp\\ipykernel_17880\\1880832876.py:13: FutureWarning: The argument 'date_parser' is deprecated and will be removed in a future version. Please use 'date_format' instead, or read your data in as 'object' dtype and then call 'to_datetime'.\n",
      "  dataset = read_csv('data/空气质量预测/raw.csv', parse_dates=[['year', 'month', 'day', 'hour']], index_col=0,\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "                     No  pm2.5  DEWP  TEMP    PRES cbwd    Iws  Is  Ir\n",
      "year_month_day_hour                                                   \n",
      "2010-01-01 00:00:00   1    NaN   -21 -11.0  1021.0   NW   1.79   0   0\n",
      "2010-01-01 01:00:00   2    NaN   -21 -12.0  1020.0   NW   4.92   0   0\n",
      "2010-01-01 02:00:00   3    NaN   -21 -11.0  1019.0   NW   6.71   0   0\n",
      "2010-01-01 03:00:00   4    NaN   -21 -14.0  1019.0   NW   9.84   0   0\n",
      "2010-01-01 04:00:00   5    NaN   -20 -12.0  1018.0   NW  12.97   0   0\n",
      "                     pm2.5  DEWP  TEMP    PRES cbwd    Iws  Is  Ir\n",
      "year_month_day_hour                                               \n",
      "2010-01-01 00:00:00    NaN   -21 -11.0  1021.0   NW   1.79   0   0\n",
      "2010-01-01 01:00:00    NaN   -21 -12.0  1020.0   NW   4.92   0   0\n",
      "2010-01-01 02:00:00    NaN   -21 -11.0  1019.0   NW   6.71   0   0\n",
      "2010-01-01 03:00:00    NaN   -21 -14.0  1019.0   NW   9.84   0   0\n",
      "2010-01-01 04:00:00    NaN   -20 -12.0  1018.0   NW  12.97   0   0\n"
     ]
    }
   ],
   "execution_count": 57
  },
  {
   "metadata": {
    "ExecuteTime": {
     "end_time": "2024-05-29T09:06:58.197958Z",
     "start_time": "2024-05-29T09:06:58.184957Z"
    }
   },
   "cell_type": "code",
   "source": [
    "# 手动指定列名，将列名设置为['pollution', 'dew', 'temp', 'press', 'wnd_dir', 'wnd_spd', 'snow', 'rain']\n",
    "dataset.columns = ['pollution', 'dew', 'temp', 'press', 'wnd_dir', 'wnd_spd', 'snow', 'rain']\n",
    "print(dataset.head(5))\n",
    "\n",
    "dataset.index.name = 'date'\n",
    "print(dataset.head(5))"
   ],
   "id": "2a4e3c1380706531",
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "                     pollution  dew  temp   press wnd_dir  wnd_spd  snow  rain\n",
      "year_month_day_hour                                                           \n",
      "2010-01-01 00:00:00        NaN  -21 -11.0  1021.0      NW     1.79     0     0\n",
      "2010-01-01 01:00:00        NaN  -21 -12.0  1020.0      NW     4.92     0     0\n",
      "2010-01-01 02:00:00        NaN  -21 -11.0  1019.0      NW     6.71     0     0\n",
      "2010-01-01 03:00:00        NaN  -21 -14.0  1019.0      NW     9.84     0     0\n",
      "2010-01-01 04:00:00        NaN  -20 -12.0  1018.0      NW    12.97     0     0\n",
      "                     pollution  dew  temp   press wnd_dir  wnd_spd  snow  rain\n",
      "date                                                                          \n",
      "2010-01-01 00:00:00        NaN  -21 -11.0  1021.0      NW     1.79     0     0\n",
      "2010-01-01 01:00:00        NaN  -21 -12.0  1020.0      NW     4.92     0     0\n",
      "2010-01-01 02:00:00        NaN  -21 -11.0  1019.0      NW     6.71     0     0\n",
      "2010-01-01 03:00:00        NaN  -21 -14.0  1019.0      NW     9.84     0     0\n",
      "2010-01-01 04:00:00        NaN  -20 -12.0  1018.0      NW    12.97     0     0\n"
     ]
    }
   ],
   "execution_count": 58
  },
  {
   "metadata": {
    "ExecuteTime": {
     "end_time": "2024-05-29T09:06:58.389006Z",
     "start_time": "2024-05-29T09:06:58.200959Z"
    }
   },
   "cell_type": "code",
   "source": [
    "# 使用fillna函数将'pollution'列中的所有NA值替换为0，inplace=True表示原地修改\n",
    "dataset['pollution'].fillna(0, inplace=True)\n",
    "print(dataset.head(5))\n",
    "\n",
    "dataset = dataset[24:]\n",
    "print(dataset.head(5))\n",
    "\n",
    "dataset.to_csv('data/空气质量预测/pollution.csv')"
   ],
   "id": "20a2347f20cbfb63",
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "                     pollution  dew  temp   press wnd_dir  wnd_spd  snow  rain\n",
      "date                                                                          \n",
      "2010-01-01 00:00:00        0.0  -21 -11.0  1021.0      NW     1.79     0     0\n",
      "2010-01-01 01:00:00        0.0  -21 -12.0  1020.0      NW     4.92     0     0\n",
      "2010-01-01 02:00:00        0.0  -21 -11.0  1019.0      NW     6.71     0     0\n",
      "2010-01-01 03:00:00        0.0  -21 -14.0  1019.0      NW     9.84     0     0\n",
      "2010-01-01 04:00:00        0.0  -20 -12.0  1018.0      NW    12.97     0     0\n",
      "                     pollution  dew  temp   press wnd_dir  wnd_spd  snow  rain\n",
      "date                                                                          \n",
      "2010-01-02 00:00:00      129.0  -16  -4.0  1020.0      SE     1.79     0     0\n",
      "2010-01-02 01:00:00      148.0  -15  -4.0  1020.0      SE     2.68     0     0\n",
      "2010-01-02 02:00:00      159.0  -11  -5.0  1021.0      SE     3.57     0     0\n",
      "2010-01-02 03:00:00      181.0   -7  -5.0  1022.0      SE     5.36     1     0\n",
      "2010-01-02 04:00:00      138.0   -7  -5.0  1022.0      SE     6.25     2     0\n"
     ]
    }
   ],
   "execution_count": 59
  },
  {
   "metadata": {},
   "cell_type": "markdown",
   "source": "## 数据画图显示",
   "id": "9abef730e3975315"
  },
  {
   "metadata": {
    "ExecuteTime": {
     "end_time": "2024-05-29T09:06:59.180782Z",
     "start_time": "2024-05-29T09:06:58.391041Z"
    }
   },
   "cell_type": "code",
   "source": [
    "from pandas import read_csv\n",
    "from matplotlib import pyplot\n",
    "\n",
    "dataset = read_csv('data/空气质量预测/pollution.csv', header=0, index_col=0)\n",
    "values = dataset.values\n",
    "groups = [0, 1, 2, 3, 5, 6, 7]\n",
    "\n",
    "i = 1\n",
    "pyplot.figure()  # 创建一个图形\n",
    "print(values)\n",
    "\n",
    "for group in groups:\n",
    "    pyplot.subplot(len(groups), 1, i)  # 创建一个子图\n",
    "    pyplot.plot(values[:, group])  # 绘制数据\n",
    "    pyplot.title(dataset.columns[group], y=0.5, loc='right')  # 设置标题\n",
    "    i += 1  # 更新i用来设置下一个子图\n",
    "\n",
    "pyplot.show()"
   ],
   "id": "25911482918bd669",
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[[129.0 -16 -4.0 ... 1.79 0 0]\n",
      " [148.0 -15 -4.0 ... 2.68 0 0]\n",
      " [159.0 -11 -5.0 ... 3.57 0 0]\n",
      " ...\n",
      " [10.0 -22 -3.0 ... 242.7 0 0]\n",
      " [8.0 -22 -4.0 ... 246.72 0 0]\n",
      " [12.0 -21 -3.0 ... 249.85 0 0]]\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<Figure size 640x480 with 7 Axes>"
      ],
      "image/png": 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"
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "execution_count": 60
  },
  {
   "metadata": {},
   "cell_type": "markdown",
   "source": "## 数据转换成监督学习数据",
   "id": "ff2a3fcbb91813c7"
  },
  {
   "metadata": {
    "ExecuteTime": {
     "end_time": "2024-05-29T09:06:59.196782Z",
     "start_time": "2024-05-29T09:06:59.183784Z"
    }
   },
   "cell_type": "code",
   "source": [
    "from pandas import read_csv\n",
    "from pandas import DataFrame\n",
    "from pandas import concat\n",
    "from sklearn.preprocessing import MinMaxScaler\n",
    "from sklearn.preprocessing import LabelEncoder\n",
    "import pandas as pd\n",
    "\n",
    "pd.set_option('display.max_columns', 1000)  # 设置显示的最大列数\n",
    "pd.set_option('display.width', 1000)  # 设置显示的最大宽度\n",
    "pd.set_option('display.max_colwidth', 1000)  # 设置显示的最大列宽"
   ],
   "id": "e88a15b4ae2887e1",
   "outputs": [],
   "execution_count": 61
  },
  {
   "metadata": {
    "ExecuteTime": {
     "end_time": "2024-05-29T09:06:59.275798Z",
     "start_time": "2024-05-29T09:06:59.198783Z"
    }
   },
   "cell_type": "code",
   "source": [
    "dataset = read_csv('data/空气质量预测/pollution.csv', header=0, index_col=0)\n",
    "values = dataset.values\n",
    "print(values)"
   ],
   "id": "5baa91074bcda2d2",
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[[129.0 -16 -4.0 ... 1.79 0 0]\n",
      " [148.0 -15 -4.0 ... 2.68 0 0]\n",
      " [159.0 -11 -5.0 ... 3.57 0 0]\n",
      " ...\n",
      " [10.0 -22 -3.0 ... 242.7 0 0]\n",
      " [8.0 -22 -4.0 ... 246.72 0 0]\n",
      " [12.0 -21 -3.0 ... 249.85 0 0]]\n"
     ]
    }
   ],
   "execution_count": 62
  },
  {
   "metadata": {
    "ExecuteTime": {
     "end_time": "2024-05-29T09:06:59.307800Z",
     "start_time": "2024-05-29T09:06:59.277783Z"
    }
   },
   "cell_type": "code",
   "source": [
    "encoder = LabelEncoder()  # 创建一个LabelEncoder对象\n",
    "values[:, 4] = encoder.fit_transform(values[:, 4])  # 将'wnd_dir'列的值转换为数字\n",
    "print(values)\n",
    "print()\n",
    "values = values.astype('float32')  # 将数据转换为浮点数\n",
    "scaler = MinMaxScaler(feature_range=(0, 1))  # 创建一个MinMaxScaler对象\n",
    "scaled = scaler.fit_transform(values)  # 将数据缩放到0到1之间\n",
    "print(scaled)"
   ],
   "id": "81f8a1e6af5d898d",
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[[129.0 -16 -4.0 ... 1.79 0 0]\n",
      " [148.0 -15 -4.0 ... 2.68 0 0]\n",
      " [159.0 -11 -5.0 ... 3.57 0 0]\n",
      " ...\n",
      " [10.0 -22 -3.0 ... 242.7 0 0]\n",
      " [8.0 -22 -4.0 ... 246.72 0 0]\n",
      " [12.0 -21 -3.0 ... 249.85 0 0]]\n",
      "\n",
      "[[0.12977867 0.35294122 0.24590163 ... 0.00229001 0.         0.        ]\n",
      " [0.14889336 0.36764708 0.24590163 ... 0.00381099 0.         0.        ]\n",
      " [0.15995975 0.4264706  0.22950819 ... 0.00533197 0.         0.        ]\n",
      " ...\n",
      " [0.01006036 0.2647059  0.26229507 ... 0.41399646 0.         0.        ]\n",
      " [0.00804829 0.2647059  0.24590163 ... 0.4208665  0.         0.        ]\n",
      " [0.01207243 0.2794118  0.26229507 ... 0.42621556 0.         0.        ]]\n"
     ]
    }
   ],
   "execution_count": 63
  },
  {
   "metadata": {},
   "cell_type": "markdown",
   "source": "转换成监督学习数据",
   "id": "2dd0606c94ccffa5"
  },
  {
   "metadata": {
    "ExecuteTime": {
     "end_time": "2024-05-29T09:06:59.322801Z",
     "start_time": "2024-05-29T09:06:59.309783Z"
    }
   },
   "cell_type": "code",
   "source": [
    "n_in = 1\n",
    "n_out = 1\n",
    "dropnan = True\n",
    "\n",
    "n_vars = 1 if type(scaled) is list else scaled.shape[1] # 如果scaled是一个列表，则n_vars为1，否则为scaled的列数\n",
    "df = DataFrame(scaled) # 将scaled转换为DataFrame\n",
    "print(df.head())"
   ],
   "id": "e19dd2699b5fefc2",
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "          0         1         2         3         4         5         6    7\n",
      "0  0.129779  0.352941  0.245902  0.527273  0.666667  0.002290  0.000000  0.0\n",
      "1  0.148893  0.367647  0.245902  0.527273  0.666667  0.003811  0.000000  0.0\n",
      "2  0.159960  0.426471  0.229508  0.545454  0.666667  0.005332  0.000000  0.0\n",
      "3  0.182093  0.485294  0.229508  0.563637  0.666667  0.008391  0.037037  0.0\n",
      "4  0.138833  0.485294  0.229508  0.563637  0.666667  0.009912  0.074074  0.0\n"
     ]
    }
   ],
   "execution_count": 64
  },
  {
   "metadata": {
    "ExecuteTime": {
     "end_time": "2024-05-29T09:06:59.338810Z",
     "start_time": "2024-05-29T09:06:59.324783Z"
    }
   },
   "cell_type": "code",
   "source": [
    "clos, names = list(), list() # 创建一个空列表clos和names\n",
    "for i in range(n_in, 0, -1): # 创建一个从n_in到0的循环\n",
    "    clos.append(df.shift(i)) # 创建一个滞后为i的DataFrame\n",
    "    print(clos)\n",
    "    names += [('var%d(t-%d)' % (j+1, i)) for j in range(n_vars)] # 创建一个名字列表"
   ],
   "id": "60b293338c64b522",
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[              0         1         2         3         4         5         6    7\n",
      "0           NaN       NaN       NaN       NaN       NaN       NaN       NaN  NaN\n",
      "1      0.129779  0.352941  0.245902  0.527273  0.666667  0.002290  0.000000  0.0\n",
      "2      0.148893  0.367647  0.245902  0.527273  0.666667  0.003811  0.000000  0.0\n",
      "3      0.159960  0.426471  0.229508  0.545454  0.666667  0.005332  0.000000  0.0\n",
      "4      0.182093  0.485294  0.229508  0.563637  0.666667  0.008391  0.037037  0.0\n",
      "...         ...       ...       ...       ...       ...       ...       ...  ...\n",
      "43795  0.010060  0.264706  0.278689  0.763638  0.333333  0.385730  0.000000  0.0\n",
      "43796  0.008048  0.250000  0.278689  0.781818  0.333333  0.395659  0.000000  0.0\n",
      "43797  0.010060  0.264706  0.262295  0.781818  0.333333  0.405588  0.000000  0.0\n",
      "43798  0.010060  0.264706  0.262295  0.781818  0.333333  0.413996  0.000000  0.0\n",
      "43799  0.008048  0.264706  0.245902  0.781818  0.333333  0.420866  0.000000  0.0\n",
      "\n",
      "[43800 rows x 8 columns]]\n"
     ]
    }
   ],
   "execution_count": 65
  },
  {
   "metadata": {
    "ExecuteTime": {
     "end_time": "2024-05-29T09:06:59.370798Z",
     "start_time": "2024-05-29T09:06:59.340784Z"
    }
   },
   "cell_type": "code",
   "source": [
    "for i in range(0, n_out): # 创建一个从0到n_out的循环\n",
    "    clos.append(df.shift(-i)) # 创建一个超前为i的DataFrame\n",
    "    print(clos)\n",
    "    if i == 0:\n",
    "        names += [('var%d(t)' % (j+1)) for j in range(n_vars)] # 创建一个名字列表\n",
    "    else:\n",
    "        names += [('var%d(t+%d)' % (j+1, i)) for j in range(n_vars)] # 创建一个名字列表\n",
    "print(clos)"
   ],
   "id": "5d9eae2dd48022d8",
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[              0         1         2         3         4         5         6    7\n",
      "0           NaN       NaN       NaN       NaN       NaN       NaN       NaN  NaN\n",
      "1      0.129779  0.352941  0.245902  0.527273  0.666667  0.002290  0.000000  0.0\n",
      "2      0.148893  0.367647  0.245902  0.527273  0.666667  0.003811  0.000000  0.0\n",
      "3      0.159960  0.426471  0.229508  0.545454  0.666667  0.005332  0.000000  0.0\n",
      "4      0.182093  0.485294  0.229508  0.563637  0.666667  0.008391  0.037037  0.0\n",
      "...         ...       ...       ...       ...       ...       ...       ...  ...\n",
      "43795  0.010060  0.264706  0.278689  0.763638  0.333333  0.385730  0.000000  0.0\n",
      "43796  0.008048  0.250000  0.278689  0.781818  0.333333  0.395659  0.000000  0.0\n",
      "43797  0.010060  0.264706  0.262295  0.781818  0.333333  0.405588  0.000000  0.0\n",
      "43798  0.010060  0.264706  0.262295  0.781818  0.333333  0.413996  0.000000  0.0\n",
      "43799  0.008048  0.264706  0.245902  0.781818  0.333333  0.420866  0.000000  0.0\n",
      "\n",
      "[43800 rows x 8 columns],               0         1         2         3         4         5         6    7\n",
      "0      0.129779  0.352941  0.245902  0.527273  0.666667  0.002290  0.000000  0.0\n",
      "1      0.148893  0.367647  0.245902  0.527273  0.666667  0.003811  0.000000  0.0\n",
      "2      0.159960  0.426471  0.229508  0.545454  0.666667  0.005332  0.000000  0.0\n",
      "3      0.182093  0.485294  0.229508  0.563637  0.666667  0.008391  0.037037  0.0\n",
      "4      0.138833  0.485294  0.229508  0.563637  0.666667  0.009912  0.074074  0.0\n",
      "...         ...       ...       ...       ...       ...       ...       ...  ...\n",
      "43795  0.008048  0.250000  0.278689  0.781818  0.333333  0.395659  0.000000  0.0\n",
      "43796  0.010060  0.264706  0.262295  0.781818  0.333333  0.405588  0.000000  0.0\n",
      "43797  0.010060  0.264706  0.262295  0.781818  0.333333  0.413996  0.000000  0.0\n",
      "43798  0.008048  0.264706  0.245902  0.781818  0.333333  0.420866  0.000000  0.0\n",
      "43799  0.012072  0.279412  0.262295  0.781818  0.333333  0.426216  0.000000  0.0\n",
      "\n",
      "[43800 rows x 8 columns]]\n",
      "[              0         1         2         3         4         5         6    7\n",
      "0           NaN       NaN       NaN       NaN       NaN       NaN       NaN  NaN\n",
      "1      0.129779  0.352941  0.245902  0.527273  0.666667  0.002290  0.000000  0.0\n",
      "2      0.148893  0.367647  0.245902  0.527273  0.666667  0.003811  0.000000  0.0\n",
      "3      0.159960  0.426471  0.229508  0.545454  0.666667  0.005332  0.000000  0.0\n",
      "4      0.182093  0.485294  0.229508  0.563637  0.666667  0.008391  0.037037  0.0\n",
      "...         ...       ...       ...       ...       ...       ...       ...  ...\n",
      "43795  0.010060  0.264706  0.278689  0.763638  0.333333  0.385730  0.000000  0.0\n",
      "43796  0.008048  0.250000  0.278689  0.781818  0.333333  0.395659  0.000000  0.0\n",
      "43797  0.010060  0.264706  0.262295  0.781818  0.333333  0.405588  0.000000  0.0\n",
      "43798  0.010060  0.264706  0.262295  0.781818  0.333333  0.413996  0.000000  0.0\n",
      "43799  0.008048  0.264706  0.245902  0.781818  0.333333  0.420866  0.000000  0.0\n",
      "\n",
      "[43800 rows x 8 columns],               0         1         2         3         4         5         6    7\n",
      "0      0.129779  0.352941  0.245902  0.527273  0.666667  0.002290  0.000000  0.0\n",
      "1      0.148893  0.367647  0.245902  0.527273  0.666667  0.003811  0.000000  0.0\n",
      "2      0.159960  0.426471  0.229508  0.545454  0.666667  0.005332  0.000000  0.0\n",
      "3      0.182093  0.485294  0.229508  0.563637  0.666667  0.008391  0.037037  0.0\n",
      "4      0.138833  0.485294  0.229508  0.563637  0.666667  0.009912  0.074074  0.0\n",
      "...         ...       ...       ...       ...       ...       ...       ...  ...\n",
      "43795  0.008048  0.250000  0.278689  0.781818  0.333333  0.395659  0.000000  0.0\n",
      "43796  0.010060  0.264706  0.262295  0.781818  0.333333  0.405588  0.000000  0.0\n",
      "43797  0.010060  0.264706  0.262295  0.781818  0.333333  0.413996  0.000000  0.0\n",
      "43798  0.008048  0.264706  0.245902  0.781818  0.333333  0.420866  0.000000  0.0\n",
      "43799  0.012072  0.279412  0.262295  0.781818  0.333333  0.426216  0.000000  0.0\n",
      "\n",
      "[43800 rows x 8 columns]]\n"
     ]
    }
   ],
   "execution_count": 66
  },
  {
   "metadata": {
    "ExecuteTime": {
     "end_time": "2024-05-29T09:06:59.402783Z",
     "start_time": "2024-05-29T09:06:59.373784Z"
    }
   },
   "cell_type": "code",
   "source": [
    "agg = concat(clos, axis=1) # 将clos中的数据连接起来\n",
    "print(agg.head())\n",
    "agg.columns = names # 设置列名\n",
    "print(agg.head())\n",
    "if dropnan: # 如果dropnan为True\n",
    "    agg.dropna(inplace=True) # 删除含有缺失值的行\n",
    "    print(agg.head())"
   ],
   "id": "d66e09863bb51001",
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "          0         1         2         3         4         5         6    7         0         1         2         3         4         5         6    7\n",
      "0       NaN       NaN       NaN       NaN       NaN       NaN       NaN  NaN  0.129779  0.352941  0.245902  0.527273  0.666667  0.002290  0.000000  0.0\n",
      "1  0.129779  0.352941  0.245902  0.527273  0.666667  0.002290  0.000000  0.0  0.148893  0.367647  0.245902  0.527273  0.666667  0.003811  0.000000  0.0\n",
      "2  0.148893  0.367647  0.245902  0.527273  0.666667  0.003811  0.000000  0.0  0.159960  0.426471  0.229508  0.545454  0.666667  0.005332  0.000000  0.0\n",
      "3  0.159960  0.426471  0.229508  0.545454  0.666667  0.005332  0.000000  0.0  0.182093  0.485294  0.229508  0.563637  0.666667  0.008391  0.037037  0.0\n",
      "4  0.182093  0.485294  0.229508  0.563637  0.666667  0.008391  0.037037  0.0  0.138833  0.485294  0.229508  0.563637  0.666667  0.009912  0.074074  0.0\n",
      "   var1(t-1)  var2(t-1)  var3(t-1)  var4(t-1)  var5(t-1)  var6(t-1)  var7(t-1)  var8(t-1)   var1(t)   var2(t)   var3(t)   var4(t)   var5(t)   var6(t)   var7(t)  var8(t)\n",
      "0        NaN        NaN        NaN        NaN        NaN        NaN        NaN        NaN  0.129779  0.352941  0.245902  0.527273  0.666667  0.002290  0.000000      0.0\n",
      "1   0.129779   0.352941   0.245902   0.527273   0.666667   0.002290   0.000000        0.0  0.148893  0.367647  0.245902  0.527273  0.666667  0.003811  0.000000      0.0\n",
      "2   0.148893   0.367647   0.245902   0.527273   0.666667   0.003811   0.000000        0.0  0.159960  0.426471  0.229508  0.545454  0.666667  0.005332  0.000000      0.0\n",
      "3   0.159960   0.426471   0.229508   0.545454   0.666667   0.005332   0.000000        0.0  0.182093  0.485294  0.229508  0.563637  0.666667  0.008391  0.037037      0.0\n",
      "4   0.182093   0.485294   0.229508   0.563637   0.666667   0.008391   0.037037        0.0  0.138833  0.485294  0.229508  0.563637  0.666667  0.009912  0.074074      0.0\n",
      "   var1(t-1)  var2(t-1)  var3(t-1)  var4(t-1)  var5(t-1)  var6(t-1)  var7(t-1)  var8(t-1)   var1(t)   var2(t)   var3(t)   var4(t)   var5(t)   var6(t)   var7(t)  var8(t)\n",
      "1   0.129779   0.352941   0.245902   0.527273   0.666667   0.002290   0.000000        0.0  0.148893  0.367647  0.245902  0.527273  0.666667  0.003811  0.000000      0.0\n",
      "2   0.148893   0.367647   0.245902   0.527273   0.666667   0.003811   0.000000        0.0  0.159960  0.426471  0.229508  0.545454  0.666667  0.005332  0.000000      0.0\n",
      "3   0.159960   0.426471   0.229508   0.545454   0.666667   0.005332   0.000000        0.0  0.182093  0.485294  0.229508  0.563637  0.666667  0.008391  0.037037      0.0\n",
      "4   0.182093   0.485294   0.229508   0.563637   0.666667   0.008391   0.037037        0.0  0.138833  0.485294  0.229508  0.563637  0.666667  0.009912  0.074074      0.0\n",
      "5   0.138833   0.485294   0.229508   0.563637   0.666667   0.009912   0.074074        0.0  0.109658  0.485294  0.213115  0.563637  0.666667  0.011433  0.111111      0.0\n"
     ]
    }
   ],
   "execution_count": 67
  },
  {
   "metadata": {
    "ExecuteTime": {
     "end_time": "2024-05-29T09:06:59.418799Z",
     "start_time": "2024-05-29T09:06:59.405786Z"
    }
   },
   "cell_type": "code",
   "source": [
    "agg.drop(agg.columns[[9, 10, 11, 12, 13, 14, 15]], axis=1, inplace=True) # 删除不需要的列\n",
    "print(agg.head())"
   ],
   "id": "d880212962ea76",
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "   var1(t-1)  var2(t-1)  var3(t-1)  var4(t-1)  var5(t-1)  var6(t-1)  var7(t-1)  var8(t-1)   var1(t)\n",
      "1   0.129779   0.352941   0.245902   0.527273   0.666667   0.002290   0.000000        0.0  0.148893\n",
      "2   0.148893   0.367647   0.245902   0.527273   0.666667   0.003811   0.000000        0.0  0.159960\n",
      "3   0.159960   0.426471   0.229508   0.545454   0.666667   0.005332   0.000000        0.0  0.182093\n",
      "4   0.182093   0.485294   0.229508   0.563637   0.666667   0.008391   0.037037        0.0  0.138833\n",
      "5   0.138833   0.485294   0.229508   0.563637   0.666667   0.009912   0.074074        0.0  0.109658\n"
     ]
    }
   ],
   "execution_count": 68
  },
  {
   "metadata": {
    "ExecuteTime": {
     "end_time": "2024-05-29T09:06:59.434800Z",
     "start_time": "2024-05-29T09:06:59.420783Z"
    }
   },
   "cell_type": "code",
   "source": [
    "values = agg.values # 将agg转换为numpy数组\n",
    "print(values)"
   ],
   "id": "b4030b61e990c018",
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[[0.12977867 0.35294122 0.24590163 ... 0.         0.         0.14889336]\n",
      " [0.14889336 0.36764708 0.24590163 ... 0.         0.         0.15995975]\n",
      " [0.15995975 0.4264706  0.22950819 ... 0.         0.         0.18209255]\n",
      " ...\n",
      " [0.01006036 0.2647059  0.26229507 ... 0.         0.         0.01006036]\n",
      " [0.01006036 0.2647059  0.26229507 ... 0.         0.         0.00804829]\n",
      " [0.00804829 0.2647059  0.24590163 ... 0.         0.         0.01207243]]\n"
     ]
    }
   ],
   "execution_count": 69
  },
  {
   "metadata": {
    "ExecuteTime": {
     "end_time": "2024-05-29T09:06:59.450799Z",
     "start_time": "2024-05-29T09:06:59.438785Z"
    }
   },
   "cell_type": "code",
   "source": [
    "n_train_hours = 365 * 24\n",
    "train = values[:n_train_hours, :] # 创建一个训练集\n",
    "test = values[n_train_hours:, :] # 创建一个测试集\n",
    "print(train.shape, test.shape)"
   ],
   "id": "1d13e93e6a03be8b",
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(8760, 9) (35039, 9)\n"
     ]
    }
   ],
   "execution_count": 70
  },
  {
   "metadata": {
    "ExecuteTime": {
     "end_time": "2024-05-29T09:06:59.466798Z",
     "start_time": "2024-05-29T09:06:59.452784Z"
    }
   },
   "cell_type": "code",
   "source": [
    "train_X, train_y = train[:, :-1], train[:, -1] # 创建一个训练集的特征和标签\n",
    "test_X, test_y = test[:, :-1], test[:, -1] # 创建一个测试集的特征和标签\n",
    "print(train_X.shape, len(train_X))"
   ],
   "id": "742513a6cf7432d1",
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(8760, 8) 8760\n"
     ]
    }
   ],
   "execution_count": 71
  },
  {
   "metadata": {
    "ExecuteTime": {
     "end_time": "2024-05-29T09:06:59.482785Z",
     "start_time": "2024-05-29T09:06:59.469786Z"
    }
   },
   "cell_type": "code",
   "source": [
    "train_X = train_X.reshape((train_X.shape[0], 1, train_X.shape[1])) # 将训练集的特征转换为三维数组\n",
    "test_X = test_X.reshape((test_X.shape[0], 1, test_X.shape[1])) # 将测试集的特征转换为三维数组\n",
    "print(train_X.shape, test_X.shape)"
   ],
   "id": "50f29424e4886448",
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(8760, 1, 8) (35039, 1, 8)\n"
     ]
    }
   ],
   "execution_count": 72
  },
  {
   "metadata": {
    "ExecuteTime": {
     "end_time": "2024-05-29T09:07:44.126458Z",
     "start_time": "2024-05-29T09:06:59.484784Z"
    }
   },
   "cell_type": "code",
   "source": [
    "from keras.src.layers import LSTM, Dense\n",
    "from keras import Sequential\n",
    "\n",
    "model = Sequential() # 创建一个Sequential模型\n",
    "# model.add(LSTM(50, activation='relu', input_shape=(train_X.shape[1], train_X.shape[2]))) # 添加一个LSTM层\n",
    "model.add(LSTM(50, input_shape=(train_X.shape[1], train_X.shape[2]))) # 添加一个LSTM层\n",
    "# model.add(LSTM(50, activation='relu', input_shape=(train_X.shape[1], train_X.shape[2]))) # 添加一个LSTM层\n",
    "\n",
    "model.add(Dense(1)) # 添加一个全连接层\n",
    "\n",
    "model.compile(optimizer='adam', loss='mse') # 编译模型\n",
    "\n",
    "history = model.fit(train_X, train_y, epochs=50, batch_size=72, verbose=2, validation_data=(test_X, test_y), shuffle=False) # 训练模型\n",
    "\n",
    "pyplot.plot(history.history['loss'], label='train') # 绘制训练集的损失曲线\n",
    "pyplot.plot(history.history['val_loss'], label='test') # 绘制测试集的损失曲线\n",
    "pyplot.legend() # 显示图例\n",
    "pyplot.show() # 显示图形"
   ],
   "id": "db1f21f561e8e38d",
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 1/50\n",
      "122/122 - 3s - loss: 0.0071 - val_loss: 0.0068 - 3s/epoch - 23ms/step\n",
      "Epoch 2/50\n",
      "122/122 - 1s - loss: 0.0038 - val_loss: 0.0077 - 647ms/epoch - 5ms/step\n",
      "Epoch 3/50\n",
      "122/122 - 1s - loss: 0.0020 - val_loss: 0.0080 - 714ms/epoch - 6ms/step\n",
      "Epoch 4/50\n",
      "122/122 - 1s - loss: 0.0013 - val_loss: 0.0069 - 828ms/epoch - 7ms/step\n",
      "Epoch 5/50\n",
      "122/122 - 1s - loss: 0.0012 - val_loss: 0.0055 - 622ms/epoch - 5ms/step\n",
      "Epoch 6/50\n",
      "122/122 - 1s - loss: 0.0011 - val_loss: 0.0044 - 654ms/epoch - 5ms/step\n",
      "Epoch 7/50\n",
      "122/122 - 1s - loss: 0.0010 - val_loss: 0.0037 - 642ms/epoch - 5ms/step\n",
      "Epoch 8/50\n",
      "122/122 - 1s - loss: 0.0010 - val_loss: 0.0032 - 600ms/epoch - 5ms/step\n",
      "Epoch 9/50\n",
      "122/122 - 1s - loss: 9.9574e-04 - val_loss: 0.0029 - 682ms/epoch - 6ms/step\n",
      "Epoch 10/50\n",
      "122/122 - 1s - loss: 9.8682e-04 - val_loss: 0.0027 - 838ms/epoch - 7ms/step\n",
      "Epoch 11/50\n",
      "122/122 - 1s - loss: 9.8093e-04 - val_loss: 0.0026 - 715ms/epoch - 6ms/step\n",
      "Epoch 12/50\n",
      "122/122 - 1s - loss: 9.7691e-04 - val_loss: 0.0025 - 649ms/epoch - 5ms/step\n",
      "Epoch 13/50\n",
      "122/122 - 1s - loss: 9.7406e-04 - val_loss: 0.0023 - 806ms/epoch - 7ms/step\n",
      "Epoch 14/50\n",
      "122/122 - 1s - loss: 9.7194e-04 - val_loss: 0.0022 - 605ms/epoch - 5ms/step\n",
      "Epoch 15/50\n",
      "122/122 - 1s - loss: 9.7023e-04 - val_loss: 0.0021 - 711ms/epoch - 6ms/step\n",
      "Epoch 16/50\n",
      "122/122 - 1s - loss: 9.6873e-04 - val_loss: 0.0020 - 843ms/epoch - 7ms/step\n",
      "Epoch 17/50\n",
      "122/122 - 1s - loss: 9.6732e-04 - val_loss: 0.0019 - 796ms/epoch - 7ms/step\n",
      "Epoch 18/50\n",
      "122/122 - 1s - loss: 9.6596e-04 - val_loss: 0.0018 - 704ms/epoch - 6ms/step\n",
      "Epoch 19/50\n",
      "122/122 - 1s - loss: 9.6469e-04 - val_loss: 0.0017 - 712ms/epoch - 6ms/step\n",
      "Epoch 20/50\n",
      "122/122 - 1s - loss: 9.6358e-04 - val_loss: 0.0016 - 709ms/epoch - 6ms/step\n",
      "Epoch 21/50\n",
      "122/122 - 1s - loss: 9.6265e-04 - val_loss: 0.0016 - 755ms/epoch - 6ms/step\n",
      "Epoch 22/50\n",
      "122/122 - 1s - loss: 9.6193e-04 - val_loss: 0.0015 - 909ms/epoch - 7ms/step\n",
      "Epoch 23/50\n",
      "122/122 - 1s - loss: 9.6139e-04 - val_loss: 0.0014 - 842ms/epoch - 7ms/step\n",
      "Epoch 24/50\n",
      "122/122 - 1s - loss: 9.6100e-04 - val_loss: 0.0014 - 759ms/epoch - 6ms/step\n",
      "Epoch 25/50\n",
      "122/122 - 1s - loss: 9.6070e-04 - val_loss: 0.0014 - 695ms/epoch - 6ms/step\n",
      "Epoch 26/50\n",
      "122/122 - 1s - loss: 9.6047e-04 - val_loss: 0.0013 - 695ms/epoch - 6ms/step\n",
      "Epoch 27/50\n",
      "122/122 - 1s - loss: 9.6027e-04 - val_loss: 0.0013 - 836ms/epoch - 7ms/step\n",
      "Epoch 28/50\n",
      "122/122 - 1s - loss: 9.6009e-04 - val_loss: 0.0013 - 870ms/epoch - 7ms/step\n",
      "Epoch 29/50\n",
      "122/122 - 1s - loss: 9.5991e-04 - val_loss: 0.0012 - 712ms/epoch - 6ms/step\n",
      "Epoch 30/50\n",
      "122/122 - 1s - loss: 9.5971e-04 - val_loss: 0.0012 - 787ms/epoch - 6ms/step\n",
      "Epoch 31/50\n",
      "122/122 - 1s - loss: 9.5948e-04 - val_loss: 0.0012 - 804ms/epoch - 7ms/step\n",
      "Epoch 32/50\n",
      "122/122 - 1s - loss: 9.5921e-04 - val_loss: 0.0012 - 772ms/epoch - 6ms/step\n",
      "Epoch 33/50\n",
      "122/122 - 1s - loss: 9.5890e-04 - val_loss: 0.0012 - 753ms/epoch - 6ms/step\n",
      "Epoch 34/50\n",
      "122/122 - 1s - loss: 9.5858e-04 - val_loss: 0.0012 - 758ms/epoch - 6ms/step\n",
      "Epoch 35/50\n",
      "122/122 - 1s - loss: 9.5823e-04 - val_loss: 0.0012 - 741ms/epoch - 6ms/step\n",
      "Epoch 36/50\n",
      "122/122 - 1s - loss: 9.5787e-04 - val_loss: 0.0011 - 734ms/epoch - 6ms/step\n",
      "Epoch 37/50\n",
      "122/122 - 1s - loss: 9.5750e-04 - val_loss: 0.0011 - 699ms/epoch - 6ms/step\n",
      "Epoch 38/50\n",
      "122/122 - 1s - loss: 9.5713e-04 - val_loss: 0.0011 - 760ms/epoch - 6ms/step\n",
      "Epoch 39/50\n",
      "122/122 - 1s - loss: 9.5675e-04 - val_loss: 0.0011 - 659ms/epoch - 5ms/step\n",
      "Epoch 40/50\n",
      "122/122 - 1s - loss: 9.5638e-04 - val_loss: 0.0011 - 736ms/epoch - 6ms/step\n",
      "Epoch 41/50\n",
      "122/122 - 1s - loss: 9.5601e-04 - val_loss: 0.0011 - 616ms/epoch - 5ms/step\n",
      "Epoch 42/50\n",
      "122/122 - 1s - loss: 9.5566e-04 - val_loss: 0.0011 - 609ms/epoch - 5ms/step\n",
      "Epoch 43/50\n",
      "122/122 - 1s - loss: 9.5531e-04 - val_loss: 0.0011 - 1s/epoch - 9ms/step\n",
      "Epoch 44/50\n",
      "122/122 - 1s - loss: 9.5497e-04 - val_loss: 0.0011 - 1s/epoch - 11ms/step\n",
      "Epoch 45/50\n",
      "122/122 - 1s - loss: 9.5464e-04 - val_loss: 0.0011 - 1s/epoch - 11ms/step\n",
      "Epoch 46/50\n",
      "122/122 - 2s - loss: 9.5433e-04 - val_loss: 0.0011 - 2s/epoch - 15ms/step\n",
      "Epoch 47/50\n",
      "122/122 - 2s - loss: 9.5403e-04 - val_loss: 0.0011 - 2s/epoch - 13ms/step\n",
      "Epoch 48/50\n",
      "122/122 - 2s - loss: 9.5374e-04 - val_loss: 0.0011 - 2s/epoch - 14ms/step\n",
      "Epoch 49/50\n",
      "122/122 - 2s - loss: 9.5346e-04 - val_loss: 0.0011 - 2s/epoch - 13ms/step\n",
      "Epoch 50/50\n",
      "122/122 - 1s - loss: 9.5319e-04 - val_loss: 0.0011 - 848ms/epoch - 7ms/step\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ],
      "image/png": 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"
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "execution_count": 73
  },
  {
   "metadata": {},
   "cell_type": "markdown",
   "source": "## 预测(一天预测一天)",
   "id": "c743110d2e73219b"
  },
  {
   "metadata": {
    "ExecuteTime": {
     "end_time": "2024-05-29T09:09:44.374473Z",
     "start_time": "2024-05-29T09:09:42.741142Z"
    }
   },
   "cell_type": "code",
   "source": [
    "yhat_test = model.predict(test_X) # 预测测试集的结果\n",
    "print(yhat_test[:10, :])\n",
    "\n",
    "print(test_X[0:6, :])"
   ],
   "id": "3e31b291970272dc",
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "1095/1095 [==============================] - 1s 1ms/step\n",
      "[[0.0322089 ]\n",
      " [0.03159105]\n",
      " [0.02111293]\n",
      " [0.01697933]\n",
      " [0.01950288]\n",
      " [0.01800634]\n",
      " [0.01243127]\n",
      " [0.01573312]\n",
      " [0.0227061 ]\n",
      " [0.0221218 ]]\n",
      "[[[0.0362173  0.33823532 0.1967213  0.83636475 0.33333334 0.12757413\n",
      "   0.         0.        ]]\n",
      "\n",
      " [[0.03118712 0.33823532 0.1967213  0.83636475 0.         0.006101\n",
      "   0.         0.        ]]\n",
      "\n",
      " [[0.02012072 0.33823532 0.1967213  0.83636475 0.         0.0145091\n",
      "   0.         0.        ]]\n",
      "\n",
      " [[0.01911469 0.32352942 0.18032786 0.83636475 0.33333334 0.006101\n",
      "   0.         0.        ]]\n",
      "\n",
      " [[0.01810865 0.32352942 0.18032786 0.83636475 0.         0.006101\n",
      "   0.         0.        ]]\n",
      "\n",
      " [[0.01710262 0.32352942 0.1967213  0.83636475 0.         0.01297103\n",
      "   0.         0.        ]]]\n"
     ]
    }
   ],
   "execution_count": 77
  },
  {
   "metadata": {
    "ExecuteTime": {
     "end_time": "2024-05-29T09:10:18.723669Z",
     "start_time": "2024-05-29T09:10:18.700545Z"
    }
   },
   "cell_type": "code",
   "source": [
    "from numpy import concatenate\n",
    "\n",
    "test_X = test_X.reshape((test_X.shape[0], test_X.shape[2])) # 将测试集的特征转换为二维数组\n",
    "inv_yhat = concatenate((yhat_test, test_X[:, 1:]), axis=1) # 将预测结果拼接起来\n",
    "inv_yhat = scaler.inverse_transform(inv_yhat) # 将预测结果还原成原始值\n",
    "inv_yhat = inv_yhat[:, 0] # 获取预测值\n",
    "print(inv_yhat)"
   ],
   "id": "a85cc0f7983cb64",
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[32.015648  31.401508  20.986252  ...  4.7611775  4.7878184  3.3710732]\n"
     ]
    }
   ],
   "execution_count": 79
  },
  {
   "metadata": {
    "ExecuteTime": {
     "end_time": "2024-05-29T09:10:22.797585Z",
     "start_time": "2024-05-29T09:10:22.778304Z"
    }
   },
   "cell_type": "code",
   "source": [
    "test_y = test_y.reshape((len(test_y), 1)) # 将测试集的标签转换为二维数组\n",
    "inv_y = concatenate((test_y, test_X[:, 1:]), axis=1) # 将真实值拼接起来\n",
    "inv_y = scaler.inverse_transform(inv_y) # 将真实值还原成原始值\n",
    "inv_y = inv_y[:, 0] # 获取真实值\n",
    "print(inv_y)"
   ],
   "id": "d197cf63c150a8fb",
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[31. 20. 19. ... 10.  8. 12.]\n"
     ]
    }
   ],
   "execution_count": 80
  },
  {
   "metadata": {
    "ExecuteTime": {
     "end_time": "2024-05-29T09:10:31.534243Z",
     "start_time": "2024-05-29T09:10:31.511245Z"
    }
   },
   "cell_type": "code",
   "source": [
    "import numpy as np\n",
    "from sklearn.metrics import mean_squared_error\n",
    "\n",
    "rmse = np.sqrt(mean_squared_error(inv_y, inv_yhat)) # 计算均方根误差\n",
    "print('Test RMSE: %.3f' % rmse)"
   ],
   "id": "4b2eb6ede301ee45",
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Test RMSE: 32.637\n"
     ]
    }
   ],
   "execution_count": 81
  },
  {
   "metadata": {
    "ExecuteTime": {
     "end_time": "2024-05-29T09:10:34.342363Z",
     "start_time": "2024-05-29T09:10:34.114346Z"
    }
   },
   "cell_type": "code",
   "source": [
    "import matplotlib.pyplot as plt\n",
    "\n",
    "# 绘制预测结果和真实值的对比图\n",
    "plt.plot(inv_y, color='blue', label='real')\n",
    "plt.plot(inv_yhat, color='red', label='predicted')\n",
    "plt.legend()\n",
    "plt.show()"
   ],
   "id": "d859d2c4a0850e2e",
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ],
      "image/png": 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"
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "execution_count": 82
  },
  {
   "metadata": {},
   "cell_type": "code",
   "source": "",
   "id": "aa26e0619d20a377",
   "outputs": [],
   "execution_count": null
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 2
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython2",
   "version": "2.7.6"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}
